





"""simple docstring"""





import argparse

import torch

from diffusers import UNetaDConditionModel


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[Any]							,						SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						List[Any]							,						SCREAMING_SNAKE_CASE					:						List[str]=None							,						SCREAMING_SNAKE_CASE					:						Dict=None							,						SCREAMING_SNAKE_CASE					:						Dict=None      ):







      '''simple docstring'''




      assert isinstance(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      ), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  lowerCAmelCase  						=	old_checkpoint[path]
                  lowerCAmelCase  						=	old_tensor.shape[0] // 3

                  lowerCAmelCase  						=	(-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

                  lowerCAmelCase  						=	old_tensor.shape[0] // config["""num_head_channels"""] // 3

                  lowerCAmelCase  						=	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]      )
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=	old_tensor.split(channels // num_heads							,						dim=1      )

                  lowerCAmelCase  						=	query.reshape(SCREAMING_SNAKE_CASE      )
                  lowerCAmelCase  						=	key.reshape(SCREAMING_SNAKE_CASE      )
                  lowerCAmelCase  						=	value.reshape(SCREAMING_SNAKE_CASE      )

      for path in paths:
            lowerCAmelCase  						=	path["""new"""]

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        lowerCAmelCase  						=	new_path.replace(replacement["""old"""]							,						replacement["""new"""]      )

        # proj_attn.weight has to be converted from conv 1D to linear
            lowerCAmelCase  						=	old_checkpoint[path["""old"""]]
            lowerCAmelCase  						=	["""proj_attn.weight"""]
            lowerCAmelCase  						=	["""proj_out.weight""", """proj_in.weight"""]
            if any(k in new_path for k in names      ):
                  lowerCAmelCase  						=	weight[:, :, 0]
            elif any(k in new_path for k in names_a      ) and len(weight.shape      ) > 2 and ".attentions." not in new_path:
                  lowerCAmelCase  						=	weight[:, :, 0]
            else:
                  lowerCAmelCase  						=	weight


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Union[str, Any]							,						SCREAMING_SNAKE_CASE					:						List[Any]=0      ):







      '''simple docstring'''




      lowerCAmelCase  						=	[]
      for old_item in old_list:
            lowerCAmelCase  						=	old_item

            #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
            #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

            #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
            #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

            #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

            mapping.append({"""old""": old_item, """new""": new_item}      )

      return mapping


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[int]							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]=1      ):







      '''simple docstring'''




      if n_shave_prefix_segments >= 0:
            return ".".join(path.split("""."""      )[n_shave_prefix_segments:]      )
      else:
            return ".".join(path.split("""."""      )[:n_shave_prefix_segments]      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Dict							,						SCREAMING_SNAKE_CASE					:						List[Any]=0      ):







      '''simple docstring'''




      lowerCAmelCase  						=	[]
      for old_item in old_list:
            mapping.append({"""old""": old_item, """new""": old_item}      )

      return mapping


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						List[str]=0      ):







      '''simple docstring'''




      lowerCAmelCase  						=	[]
      for old_item in old_list:
            lowerCAmelCase  						=	old_item.replace("""in_layers.0"""							,						"""norm1"""      )
            lowerCAmelCase  						=	new_item.replace("""in_layers.2"""							,						"""conv1"""      )

            lowerCAmelCase  						=	new_item.replace("""out_layers.0"""							,						"""norm2"""      )
            lowerCAmelCase  						=	new_item.replace("""out_layers.3"""							,						"""conv2"""      )

            lowerCAmelCase  						=	new_item.replace("""emb_layers.1"""							,						"""time_emb_proj"""      )
            lowerCAmelCase  						=	new_item.replace("""skip_connection"""							,						"""conv_shortcut"""      )

            lowerCAmelCase  						=	shave_segments(SCREAMING_SNAKE_CASE							,						n_shave_prefix_segments=SCREAMING_SNAKE_CASE      )

            if "temopral_conv" not in old_item:
                  mapping.append({"""old""": old_item, """new""": new_item}      )

      return mapping


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						str							,						SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						List[Any]=None							,						SCREAMING_SNAKE_CASE					:						Optional[int]=False      ):







      '''simple docstring'''




      lowerCAmelCase  						=	{}
      lowerCAmelCase  						=	list(checkpoint.keys()      )

      lowerCAmelCase  						=	"""model.diffusion_model."""

      # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
      if sum(k.startswith("""model_ema"""      ) for k in keys      ) > 1_00 and extract_ema:
            print(F'Checkpoint {path} has both EMA and non-EMA weights.'      )
            print(
                """In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"""
                """ weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."""      )
            for key in keys:
                  if key.startswith("""model.diffusion_model"""      ):
                        lowerCAmelCase  						=	"""model_ema.""" + """""".join(key.split("""."""      )[1:]      )
                        lowerCAmelCase  						=	checkpoint.pop(SCREAMING_SNAKE_CASE      )
      else:
            if sum(k.startswith("""model_ema"""      ) for k in keys      ) > 1_00:
                  print(
                      """In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"""
                      """ weights (usually better for inference), please make sure to add the `--extract_ema` flag."""      )

            for key in keys:
                  lowerCAmelCase  						=	checkpoint.pop(SCREAMING_SNAKE_CASE      )

      lowerCAmelCase  						=	{}

      lowerCAmelCase  						=	unet_state_dict["""time_embed.0.weight"""]
      lowerCAmelCase  						=	unet_state_dict["""time_embed.0.bias"""]
      lowerCAmelCase  						=	unet_state_dict["""time_embed.2.weight"""]
      lowerCAmelCase  						=	unet_state_dict["""time_embed.2.bias"""]

      if config["class_embed_type"] is None:
            # No parameters to port
            ...
      elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
            lowerCAmelCase  						=	unet_state_dict["""label_emb.0.0.weight"""]
            lowerCAmelCase  						=	unet_state_dict["""label_emb.0.0.bias"""]
            lowerCAmelCase  						=	unet_state_dict["""label_emb.0.2.weight"""]
            lowerCAmelCase  						=	unet_state_dict["""label_emb.0.2.bias"""]
      else:
            raise NotImplementedError(F'Not implemented `class_embed_type`: {config["class_embed_type"]}'      )

      lowerCAmelCase  						=	unet_state_dict["""input_blocks.0.0.weight"""]
      lowerCAmelCase  						=	unet_state_dict["""input_blocks.0.0.bias"""]

      lowerCAmelCase  						=	[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""      )]
      lowerCAmelCase  						=	renew_attention_paths(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
      assign_to_checkpoint(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

      lowerCAmelCase  						=	unet_state_dict["""out.0.weight"""]
      lowerCAmelCase  						=	unet_state_dict["""out.0.bias"""]
      lowerCAmelCase  						=	unet_state_dict["""out.2.weight"""]
      lowerCAmelCase  						=	unet_state_dict["""out.2.bias"""]

      # Retrieves the keys for the input blocks only
      lowerCAmelCase  						=	len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """input_blocks""" in layer}      )
      lowerCAmelCase  						=	{
          layer_id: [key for key in unet_state_dict if F'input_blocks.{layer_id}' in key]
          for layer_id in range(SCREAMING_SNAKE_CASE      )
      }

      # Retrieves the keys for the middle blocks only
      lowerCAmelCase  						=	len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """middle_block""" in layer}      )
      lowerCAmelCase  						=	{
          layer_id: [key for key in unet_state_dict if F'middle_block.{layer_id}' in key]
          for layer_id in range(SCREAMING_SNAKE_CASE      )
      }

      # Retrieves the keys for the output blocks only
      lowerCAmelCase  						=	len({""".""".join(layer.split("""."""      )[:2]      ) for layer in unet_state_dict if """output_blocks""" in layer}      )
      lowerCAmelCase  						=	{
          layer_id: [key for key in unet_state_dict if F'output_blocks.{layer_id}' in key]
          for layer_id in range(SCREAMING_SNAKE_CASE      )
      }

      for i in range(1							,						SCREAMING_SNAKE_CASE      ):
            lowerCAmelCase  						=	(i - 1) // (config["""layers_per_block"""] + 1)
            lowerCAmelCase  						=	(i - 1) % (config["""layers_per_block"""] + 1)

            lowerCAmelCase  						=	[
                key for key in input_blocks[i] if F'input_blocks.{i}.0' in key and F'input_blocks.{i}.0.op' not in key
            ]
            lowerCAmelCase  						=	[key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
            lowerCAmelCase  						=	[key for key in input_blocks[i] if F'input_blocks.{i}.2' in key]

            if F'input_blocks.{i}.op.weight' in unet_state_dict:
                  lowerCAmelCase  						=	unet_state_dict.pop(
                      F'input_blocks.{i}.op.weight'      )
                  lowerCAmelCase  						=	unet_state_dict.pop(
                      F'input_blocks.{i}.op.bias'      )

            lowerCAmelCase  						=	renew_resnet_paths(SCREAMING_SNAKE_CASE      )
            lowerCAmelCase  						=	{"""old""": F'input_blocks.{i}.0', """new""": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
            assign_to_checkpoint(
                SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

            lowerCAmelCase  						=	[key for key in resnets if """temopral_conv""" in key]
            lowerCAmelCase  						=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE      )
            lowerCAmelCase  						=	{
                """old""": F'input_blocks.{i}.0.temopral_conv',
                """new""": F'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
            }
            assign_to_checkpoint(
                SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

            if len(SCREAMING_SNAKE_CASE      ):
                  lowerCAmelCase  						=	renew_attention_paths(SCREAMING_SNAKE_CASE      )
                  lowerCAmelCase  						=	{"""old""": F'input_blocks.{i}.1', """new""": F'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
                  assign_to_checkpoint(
                      SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

            if len(SCREAMING_SNAKE_CASE      ):
                  lowerCAmelCase  						=	renew_attention_paths(SCREAMING_SNAKE_CASE      )
                  lowerCAmelCase  						=	{
                      """old""": F'input_blocks.{i}.2',
                      """new""": F'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
                  }
                  assign_to_checkpoint(
                      SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

      lowerCAmelCase  						=	middle_blocks[0]
      lowerCAmelCase  						=	[key for key in resnet_a if """temopral_conv""" in key]
      lowerCAmelCase  						=	middle_blocks[1]
      lowerCAmelCase  						=	middle_blocks[2]
      lowerCAmelCase  						=	middle_blocks[3]
      lowerCAmelCase  						=	[key for key in resnet_a if """temopral_conv""" in key]

      lowerCAmelCase  						=	renew_resnet_paths(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						config=SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]      )

      lowerCAmelCase  						=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						config=SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]      )

      lowerCAmelCase  						=	renew_resnet_paths(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						config=SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]      )

      lowerCAmelCase  						=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						config=SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]      )

      lowerCAmelCase  						=	renew_attention_paths(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

      lowerCAmelCase  						=	renew_attention_paths(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
      assign_to_checkpoint(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

      for i in range(SCREAMING_SNAKE_CASE      ):
            lowerCAmelCase  						=	i // (config["""layers_per_block"""] + 1)
            lowerCAmelCase  						=	i % (config["""layers_per_block"""] + 1)
            lowerCAmelCase  						=	[shave_segments(SCREAMING_SNAKE_CASE							,						2      ) for name in output_blocks[i]]
            lowerCAmelCase  						=	{}

            for layer in output_block_layers:
                  lowerCAmelCase						,		lowerCAmelCase  						=	layer.split("""."""      )[0], shave_segments(SCREAMING_SNAKE_CASE							,						1      )
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(SCREAMING_SNAKE_CASE      )
                  else:
                        lowerCAmelCase  						=	[layer_name]

            if len(SCREAMING_SNAKE_CASE      ) > 1:
                  lowerCAmelCase  						=	[key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
                  lowerCAmelCase  						=	[key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
                  lowerCAmelCase  						=	[key for key in output_blocks[i] if F'output_blocks.{i}.2' in key]

                  lowerCAmelCase  						=	renew_resnet_paths(SCREAMING_SNAKE_CASE      )
                  lowerCAmelCase  						=	renew_resnet_paths(SCREAMING_SNAKE_CASE      )

                  lowerCAmelCase  						=	{"""old""": F'output_blocks.{i}.0', """new""": F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
                  assign_to_checkpoint(
                      SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

                  lowerCAmelCase  						=	[key for key in resnets if """temopral_conv""" in key]
                  lowerCAmelCase  						=	renew_temp_conv_paths(SCREAMING_SNAKE_CASE      )
                  lowerCAmelCase  						=	{
                      """old""": F'output_blocks.{i}.0.temopral_conv',
                      """new""": F'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
                  }
                  assign_to_checkpoint(
                      SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

                  lowerCAmelCase  						=	{k: sorted(SCREAMING_SNAKE_CASE      ) for k, v in output_block_list.items()}
                  if ["conv.bias", "conv.weight"] in output_block_list.values():
                        lowerCAmelCase  						=	list(output_block_list.values()      ).index(["""conv.bias""", """conv.weight"""]      )
                        lowerCAmelCase  						=	unet_state_dict[
                            F'output_blocks.{i}.{index}.conv.weight'
                        ]
                        lowerCAmelCase  						=	unet_state_dict[
                            F'output_blocks.{i}.{index}.conv.bias'
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(SCREAMING_SNAKE_CASE      ) == 2:
                              lowerCAmelCase  						=	[]

                  if len(SCREAMING_SNAKE_CASE      ):
                        lowerCAmelCase  						=	renew_attention_paths(SCREAMING_SNAKE_CASE      )
                        lowerCAmelCase  						=	{
                            """old""": F'output_blocks.{i}.1',
                            """new""": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                        }
                        assign_to_checkpoint(
                            SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )

                  if len(SCREAMING_SNAKE_CASE      ):
                        lowerCAmelCase  						=	renew_attention_paths(SCREAMING_SNAKE_CASE      )
                        lowerCAmelCase  						=	{
                            """old""": F'output_blocks.{i}.2',
                            """new""": F'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
                        }
                        assign_to_checkpoint(
                            SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						additional_replacements=[meta_path]							,						config=SCREAMING_SNAKE_CASE      )
            else:
                  lowerCAmelCase  						=	renew_resnet_paths(SCREAMING_SNAKE_CASE							,						n_shave_prefix_segments=1      )
                  for path in resnet_0_paths:
                        lowerCAmelCase  						=	""".""".join(["""output_blocks""", str(SCREAMING_SNAKE_CASE      ), path["""old"""]]      )
                        lowerCAmelCase  						=	""".""".join(["""up_blocks""", str(SCREAMING_SNAKE_CASE      ), """resnets""", str(SCREAMING_SNAKE_CASE      ), path["""new"""]]      )
                        lowerCAmelCase  						=	unet_state_dict[old_path]

                  lowerCAmelCase  						=	[l for l in output_block_layers if """temopral_conv""" in l]
                  for path in temopral_conv_paths:
                        lowerCAmelCase  						=	path.split("""temopral_conv."""      )[-1]
                        lowerCAmelCase  						=	""".""".join(["""output_blocks""", str(SCREAMING_SNAKE_CASE      ), str(SCREAMING_SNAKE_CASE      ), """temopral_conv""", pruned_path]      )
                        lowerCAmelCase  						=	""".""".join(["""up_blocks""", str(SCREAMING_SNAKE_CASE      ), """temp_convs""", str(SCREAMING_SNAKE_CASE      ), pruned_path]      )
                        lowerCAmelCase  						=	unet_state_dict[old_path]

      return new_checkpoint


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ =       argparse.ArgumentParser()

       parser.add_argument(
           "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
       )
       parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
       SCREAMING_SNAKE_CASE__ =       parser.parse_args()

       SCREAMING_SNAKE_CASE__ =       torch.load(args.checkpoint_path, map_location="cpu")
       SCREAMING_SNAKE_CASE__ =       UNetaDConditionModel()

       SCREAMING_SNAKE_CASE__ =       convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       SCREAMING_SNAKE_CASE__ =       set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       SCREAMING_SNAKE_CASE__ =       set(converted_ckpt.keys()) - set(unet.state_dict().keys())

       assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

       # load state_dict
       unet.load_state_dict(converted_ckpt)

       unet.save_pretrained(args.dump_path)

       # -- finish converting the unet --













"""simple docstring"""





from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel

@require_tf
class 		lowercase :
      _SCREAMING_SNAKE_CASE         =				MarianConfig
      _SCREAMING_SNAKE_CASE         =				{}
      _SCREAMING_SNAKE_CASE         =				'gelu'





      def __init__(	self				,						lowercase				,						lowercase=13				,						lowercase=7				,						lowercase=True				,						lowercase=False				,						lowercase=99				,						lowercase=32				,						lowercase=2				,						lowercase=4				,						lowercase=37				,						lowercase=0.1				,						lowercase=0.1				,						lowercase=20				,						lowercase=2				,						lowercase=1				,						lowercase=0				,						)		->					int:
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	batch_size
            lowerCAmelCase  						=	seq_length
            lowerCAmelCase  						=	is_training
            lowerCAmelCase  						=	use_labels
            lowerCAmelCase  						=	vocab_size
            lowerCAmelCase  						=	hidden_size
            lowerCAmelCase  						=	num_hidden_layers
            lowerCAmelCase  						=	num_attention_heads
            lowerCAmelCase  						=	intermediate_size

            lowerCAmelCase  						=	hidden_dropout_prob
            lowerCAmelCase  						=	attention_probs_dropout_prob
            lowerCAmelCase  						=	max_position_embeddings
            lowerCAmelCase  						=	eos_token_id
            lowerCAmelCase  						=	pad_token_id
            lowerCAmelCase  						=	bos_token_id





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	ids_tensor([self.batch_size, self.seq_length - 1]				,						self.vocab_size			)
            lowerCAmelCase  						=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)				,						1			)
            lowerCAmelCase  						=	tf.concat([input_ids, eos_tensor]				,						axis=1			)

            lowerCAmelCase  						=	ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size			)

            lowerCAmelCase  						=	self.config_cls(
                vocab_size=self.vocab_size				,						d_model=self.hidden_size				,						encoder_layers=self.num_hidden_layers				,						decoder_layers=self.num_hidden_layers				,						encoder_attention_heads=self.num_attention_heads				,						decoder_attention_heads=self.num_attention_heads				,						encoder_ffn_dim=self.intermediate_size				,						decoder_ffn_dim=self.intermediate_size				,						dropout=self.hidden_dropout_prob				,						attention_dropout=self.attention_probs_dropout_prob				,						max_position_embeddings=self.max_position_embeddings				,						eos_token_ids=[2]				,						bos_token_id=self.bos_token_id				,						pad_token_id=self.pad_token_id				,						decoder_start_token_id=self.pad_token_id				,						**self.config_updates				,						)
            lowerCAmelCase  						=	prepare_marian_inputs_dict(lowercase				,						lowercase				,						lowercase			)
            return config, inputs_dict





      def    _snake_case      (	self				,						lowercase				,						lowercase			)		->					Union[str, Any]:
            lowerCAmelCase  						=	TFMarianModel(config=lowercase			).get_decoder()
            lowerCAmelCase  						=	inputs_dict["""input_ids"""]

            lowerCAmelCase  						=	input_ids[:1, :]
            lowerCAmelCase  						=	inputs_dict["""attention_mask"""][:1, :]
            lowerCAmelCase  						=	inputs_dict["""head_mask"""]
            lowerCAmelCase  						=	1

            # first forward pass
            lowerCAmelCase  						=	model(lowercase				,						attention_mask=lowercase				,						head_mask=lowercase				,						use_cache=lowercase			)

            lowerCAmelCase						,		lowerCAmelCase  						=	outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            lowerCAmelCase  						=	ids_tensor((self.batch_size, 3)				,						config.vocab_size			)
            lowerCAmelCase  						=	tf.cast(ids_tensor((self.batch_size, 3)				,						2			)				,						tf.inta			)

            # append to next input_ids and
            lowerCAmelCase  						=	tf.concat([input_ids, next_tokens]				,						axis=-1			)
            lowerCAmelCase  						=	tf.concat([attention_mask, next_attn_mask]				,						axis=-1			)

            lowerCAmelCase  						=	model(lowercase				,						attention_mask=lowercase			)[0]
            lowerCAmelCase  						=	model(lowercase				,						attention_mask=lowercase				,						past_key_values=lowercase			)[0]

            self.parent.assertEqual(next_tokens.shape[1]				,						output_from_past.shape[1]			)

            # select random slice
            lowerCAmelCase  						=	int(ids_tensor((1,)				,						output_from_past.shape[-1]			)			)
            lowerCAmelCase  						=	output_from_no_past[:, -3:, random_slice_idx]
            lowerCAmelCase  						=	output_from_past[:, :, random_slice_idx]

            # test that outputs are equal for slice
            tf.debugging.assert_near(lowercase				,						lowercase				,						rtol=1e-3			)







def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						List[str]							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]							,						SCREAMING_SNAKE_CASE					:						Any=None							,						SCREAMING_SNAKE_CASE					:						str=None							,						SCREAMING_SNAKE_CASE					:						List[str]=None							,						SCREAMING_SNAKE_CASE					:						Tuple=None							,						SCREAMING_SNAKE_CASE					:						Dict=None							,						):







      '''simple docstring'''




      if attention_mask is None:
            lowerCAmelCase  						=	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE							,						config.pad_token_id      )							,						tf.inta      )
      if decoder_attention_mask is None:
            lowerCAmelCase  						=	tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape							,						dtype=tf.inta      ),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,						config.pad_token_id      )							,						tf.inta      ),
                ]							,						axis=-1							,						)
      if head_mask is None:
            lowerCAmelCase  						=	tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
            lowerCAmelCase  						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
      if cross_attn_head_mask is None:
            lowerCAmelCase  						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }

@require_tf
class 		lowercase ( _UpperCAmelCase					,  _UpperCAmelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE         =				(TFMarianMTModel,) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE         =				(
          {
              'conversational': TFMarianMTModel,
              'feature-extraction': TFMarianModel,
              'summarization': TFMarianMTModel,
              'text2text-generation': TFMarianMTModel,
              'translation': TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE         =				True
      _SCREAMING_SNAKE_CASE         =				False
      _SCREAMING_SNAKE_CASE         =				False





      def    _snake_case      (	self			)		->					Optional[int]:
            lowerCAmelCase  						=	TFMarianModelTester(self			)
            lowerCAmelCase  						=	ConfigTester(self				,						config_class=lowercase			)





      def    _snake_case      (	self			)		->					str:
            self.config_tester.run_common_tests()





      def    _snake_case      (	self			)		->					str:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*lowercase			)







@require_tf
class 		lowercase ( unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				1_000  # show more chars for failing integration tests





      @classmethod
      def    _snake_case      (	cls			)		->					None:
            lowerCAmelCase  						=	f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
            return cls





      @cached_property
      def    _snake_case      (	self			)		->					MarianTokenizer:
            return AutoTokenizer.from_pretrained(self.model_name			)





      @property
      def    _snake_case      (	self			)		->					int:
            return self.tokenizer.eos_token_id





      @cached_property
      def    _snake_case      (	self			)		->					Any:
            warnings.simplefilter("""error"""			)
            lowerCAmelCase  						=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
            assert isinstance(lowercase				,						lowercase			)
            lowerCAmelCase  						=	model.config
            self.assertListEqual(c.bad_words_ids				,						[[c.pad_token_id]]			)
            self.assertEqual(c.max_length				,						512			)
            self.assertEqual(c.decoder_start_token_id				,						c.pad_token_id			)
            return model





      def    _snake_case      (	self				,						**lowercase			)		->					Tuple:
            lowerCAmelCase  						=	self.translate_src_text(**lowercase			)
            self.assertListEqual(self.expected_text				,						lowercase			)





      def    _snake_case      (	self				,						**lowercase			)		->					Union[str, Any]:
            lowerCAmelCase  						=	self.tokenizer(self.src_text				,						**lowercase				,						padding=lowercase				,						return_tensors="""tf"""			)
            lowerCAmelCase  						=	self.model.generate(
                model_inputs.input_ids				,						attention_mask=model_inputs.attention_mask				,						num_beams=2				,						max_length=128			)
            lowerCAmelCase  						=	self.tokenizer.batch_decode(generated_ids.numpy()				,						skip_special_tokens=lowercase			)
            return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'mt'
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      _SCREAMING_SNAKE_CASE         =				['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']





      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def    _snake_case      (	self			)		->					List[Any]:
            self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'zh'
      _SCREAMING_SNAKE_CASE         =				['My name is Wolfgang and I live in Berlin']
      _SCREAMING_SNAKE_CASE         =				['我叫沃尔夫冈 我住在柏林']





      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def    _snake_case      (	self			)		->					Dict:
            self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'ROMANCE'
      _SCREAMING_SNAKE_CASE         =				[
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      _SCREAMING_SNAKE_CASE         =				[
          'Ne passez pas autant de temps à regarder la télé.',
          'A sua mensagem foi enviada.',
          'Es dos años más viejo que yo.',
      ]





      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def    _snake_case      (	self			)		->					List[str]:
            self._assert_generated_batch_equal_expected()





      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	TranslationPipeline(self.model				,						self.tokenizer				,						framework="""tf"""			)
            lowerCAmelCase  						=	pipeline(self.src_text			)
            self.assertEqual(self.expected_text				,						[x["""translation_text"""] for x in output]			)






