




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMModel,
		    TFXLMWithLMHeadModel,
		    XLMConfig,
		)




class    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					List[Any] ,				__lowerCamelCase    :					Any ,				) ->					Any:
							a   =      parent
							a   =      13
							a   =      7
							a   =      True
							a   =      True
							a   =      True
							a   =      True
							a   =      True
							a   =      False
							a   =      False
							a   =      False
							a   =      2
							a   =      99
							a   =      0
							a   =      32
							a   =      2
							a   =      4
							a   =      0.1
							a   =      0.1
							a   =      5_12
							a   =      16
							a   =      2
							a   =      0.02
							a   =      3
							a   =      4
							a   =      "last"
							a   =      True
							a   =      None
							a   =      0





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Tuple:
							a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size							)
							a   =      random_attention_mask([self.batch_size, self.seq_length] ,				dtype=tf.floataa							)

							a   =      None
							if self.use_input_lengths:
										a   =      (
										    ids_tensor([self.batch_size] ,				vocab_size=2							) + self.seq_length - 2
										)  # small variation of seq_length

							a   =      None
							if self.use_token_type_ids:
										a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.n_langs							)

							a   =      None
							a   =      None
							a   =      None
							if self.use_labels:
										a   =      ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)
										a   =      ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels							)
										a   =      ids_tensor([self.batch_size] ,				2 ,				dtype=tf.floataa							)
										a   =      ids_tensor([self.batch_size] ,				self.num_choices							)

							a   =      XLMConfig(
							    vocab_size=self.vocab_size ,				n_special=self.n_special ,				emb_dim=self.hidden_size ,				n_layers=self.num_hidden_layers ,				n_heads=self.num_attention_heads ,				dropout=self.hidden_dropout_prob ,				attention_dropout=self.attention_probs_dropout_prob ,				gelu_activation=self.gelu_activation ,				sinusoidal_embeddings=self.sinusoidal_embeddings ,				asm=self.asm ,				causal=self.causal ,				n_langs=self.n_langs ,				max_position_embeddings=self.max_position_embeddings ,				initializer_range=self.initializer_range ,				summary_type=self.summary_type ,				use_proj=self.use_proj ,				bos_token_id=self.bos_token_id ,				)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					str ,				) ->					List[Any]:
							a   =      TFXLMModel(config=__lowerCamelCase							)
							a   =      {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
							a   =      model(__lowerCamelCase							)

							a   =      [input_ids, input_mask]
							a   =      model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Optional[int] ,				) ->					List[str]:
							a   =      TFXLMWithLMHeadModel(__lowerCamelCase							)

							a   =      {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
							a   =      model(__lowerCamelCase							)

							a   =      outputs

							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(				self    :					Optional[int] ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Optional[int] ,				__lowerCamelCase    :					Optional[Any] ,				) ->					Dict:
							a   =      TFXLMForQuestionAnsweringSimple(__lowerCamelCase							)

							a   =      {"input_ids": input_ids, "lengths": input_lengths}

							a   =      model(__lowerCamelCase							)

							self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)							)
							self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)							)





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Tuple ,				) ->					Dict:
							a   =      TFXLMForSequenceClassification(__lowerCamelCase							)

							a   =      {"input_ids": input_ids, "lengths": input_lengths}

							a   =      model(__lowerCamelCase							)

							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)							)





				def 							__UpperCAmelCase							(				self    :					Any ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					List[Any] ,				) ->					Union[str, Any]:
							a   =      self.num_labels
							a   =      TFXLMForTokenClassification(config=__lowerCamelCase							)
							a   =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							a   =      model(__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Any ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					List[Any] ,				__lowerCamelCase    :					Dict ,				__lowerCamelCase    :					Tuple ,				) ->					Union[str, Any]:
							a   =      self.num_choices
							a   =      TFXLMForMultipleChoice(config=__lowerCamelCase							)
							a   =      tf.tile(tf.expand_dims(__lowerCamelCase ,				1							) ,				(1, self.num_choices, 1)							)
							a   =      tf.tile(tf.expand_dims(__lowerCamelCase ,				1							) ,				(1, self.num_choices, 1)							)
							a   =      tf.tile(tf.expand_dims(__lowerCamelCase ,				1							) ,				(1, self.num_choices, 1)							)
							a   =      {
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							    "token_type_ids": multiple_choice_token_type_ids,
							}
							a   =      model(__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					Dict:
							a   =      self.prepare_config_and_inputs()
							(
							    (
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		(
							    a
							)   ,		
							)   =      config_and_inputs
							a   =      {
							    "input_ids": input_ids,
							    "token_type_ids": token_type_ids,
							    "langs": token_type_ids,
							    "lengths": input_lengths,
							}
							return config, inputs_dict




@require_tf
class    snake_case__							(_UpperCamelCase   ,	_UpperCamelCase   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Optional[Any]										= (
				    (
				        TFXLMModel,
				        TFXLMWithLMHeadModel,
				        TFXLMForSequenceClassification,
				        TFXLMForQuestionAnsweringSimple,
				        TFXLMForTokenClassification,
				        TFXLMForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_	:							Tuple										= (
				    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				SCREAMING_SNAKE_CASE_	:							Optional[int]										= (
				    {
				        """feature-extraction""": TFXLMModel,
				        """fill-mask""": TFXLMWithLMHeadModel,
				        """question-answering""": TFXLMForQuestionAnsweringSimple,
				        """text-classification""": TFXLMForSequenceClassification,
				        """text-generation""": TFXLMWithLMHeadModel,
				        """token-classification""": TFXLMForTokenClassification,
				        """zero-shot""": TFXLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_	:							List[str]										= False
				SCREAMING_SNAKE_CASE_	:							Dict										= False





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					List[str] ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Optional[int]							) ->					Tuple:
							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("Fast"							)
							):
										# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
										# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
										# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
										return True

							return False





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					Any:
							a   =      TFXLMModelTester(self							)
							a   =      ConfigTester(self ,				config_class=__lowerCamelCase ,				emb_dim=37							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					int:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(				self    :					str							) ->					str:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Optional[int]:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_lm_head(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					Union[str, Any]:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_qa(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Any:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_sequence_classif(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Any:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_token_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					str:
							a   =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_xlm_for_multiple_choice(*__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Dict:
							for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a   =      TFXLMModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)




@require_tf
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					List[Any]:
							a   =      TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"							)
							a   =      tf.convert_to_tensor([[14, 4_47]] ,				dtype=tf.intaa							)  # the president
							a   =      [
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							    14,
							    4_47,
							]  # the president the president the president the president the president the president the president the president the president the president
							# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
							a   =      model.generate(__lowerCamelCase ,				do_sample=__lowerCamelCase							)
							self.assertListEqual(output_ids[0].numpy().tolist() ,				__lowerCamelCase							)












from typing import Any

import numpy as np




class    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					Tuple ,				__lowerCamelCase    :					np.ndarray ,				__lowerCamelCase    :					int							) ->					None:
							# Check if RHS is negative
							if np.any(tableau[:, -1] ,				where=tableau[:, -1] < 0							):
										raise ValueError("RHS must be > 0"							)

							a   =      tableau
							a   ,		a   =      tableau.shape

							# Number of decision variables x1, x2, x3...
							a   =      n_vars

							# Number of artificial variables to be minimised
							a   =      len(np.where(tableau[self.n_vars : -1] == -1							)[0]							)

							# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
							a   =      (self.n_art_vars > 0) + 1

							# Number of slack variables added to make inequalities into equalities
							a   =      self.n_rows - self.n_stages

							# Objectives for each stage
							a   =      ["max"]

							# In two stage simplex, first minimise then maximise
							if self.n_art_vars:
										self.objectives.append("min"							)

							a   =      [""]

							# Index of current pivot row and column
							a   =      None
							a   =      None

							# Does objective row only contain (non)-negative values?
							a   =      False





				@staticmethod
				def 							__UpperCAmelCase							(				*__lowerCamelCase    :					int							) ->					list[str]:
							if len(__lowerCamelCase							) != 3:
										raise ValueError("Must provide n_vars, n_slack, and n_art_vars"							)

							if not all(x >= 0 and isinstance(__lowerCamelCase ,				__lowerCamelCase							) for x in args							):
										raise ValueError("All arguments must be non-negative integers"							)

							# decision | slack | artificial
							a   =      ["x", "s", "a"]
							a   =      []
							for i in range(3							):
										for j in range(args[i]							):
													titles.append(string_starts[i] + str(j + 1							)							)
							titles.append("RHS"							)
							return titles





				def 							__UpperCAmelCase							(				self    :					Tuple ,				__lowerCamelCase    :					np.ndarray							) ->					tuple[Any, Any]:
							a   =      self.objectives[-1]

							# Find entries of highest magnitude in objective rows
							a   =      (objective == "min") - (objective == "max")
							a   =      np.argmax(sign * tableau[0, : self.n_vars]							)

							# Choice is only valid if below 0 for maximise, and above for minimise
							if sign * self.tableau[0, col_idx] <= 0:
										a   =      True
										return 0, 0

							# Pivot row is chosen as having the lowest quotient when elements of
							# the pivot column divide the right-hand side

							# Slice excluding the objective rows
							a   =      slice(self.n_stages ,				self.n_rows							)

							# RHS
							a   =      tableau[s, -1]

							# Elements of pivot column within slice
							a   =      tableau[s, col_idx]

							# Array filled with nans
							a   =      np.full(self.n_rows - self.n_stages ,				np.nan							)

							# If element in pivot column is greater than zeron_stages, return
							# quotient or nan otherwise
							a   =      np.divide(__lowerCamelCase ,				__lowerCamelCase ,				out=__lowerCamelCase ,				where=divisor > 0							)

							# Arg of minimum quotient excluding the nan values. n_stages is added
							# to compensate for earlier exclusion of objective columns
							a   =      np.nanargmin(__lowerCamelCase							) + self.n_stages
							return row_idx, col_idx





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					np.ndarray ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					int							) ->					np.ndarray:
							a   =      tableau[row_idx].copy()

							a   =      piv_row[col_idx]

							# Entry becomes 1
							piv_row *= 1 / piv_val

							# Variable in pivot column becomes basic, ie the only non-zero entry
							for idx, coeff in enumerate(tableau[:, col_idx]							):
										tableau[idx] += -coeff * piv_row
							a   =      piv_row
							return tableau





				def 							__UpperCAmelCase							(				self    :					Tuple ,				__lowerCamelCase    :					np.ndarray							) ->					np.ndarray:
							self.objectives.pop()

							if not self.objectives:
										return tableau

							# Slice containing ids for artificial columns
							a   =      slice(-self.n_art_vars - 1 ,				-1							)

							# Delete the artificial variable columns
							a   =      np.delete(__lowerCamelCase ,				__lowerCamelCase ,				axis=1							)

							# Delete the objective row of the first stage
							a   =      np.delete(__lowerCamelCase ,				0 ,				axis=0							)

							a   =      1
							self.n_rows -= 1
							a   =      0
							a   =      False
							return tableau





				def 							__UpperCAmelCase							(				self    :					Any							) ->					dict[Any, Any]:
							# Stop simplex algorithm from cycling.
							for _ in range(1_00							):
										# Completion of each stage removes an objective. If both stages
										# are complete, then no objectives are left
										if not self.objectives:
													a   =      self.generate_col_titles(
													    self.n_vars ,				self.n_slack ,				self.n_art_vars							)

													# Find the values of each variable at optimal solution
													return self.interpret_tableau(self.tableau ,				self.col_titles							)

										a   ,		a   =      self.find_pivot(self.tableau							)

										# If there are no more negative values in objective row
										if self.stop_iter:
													# Delete artificial variable columns and rows. Update attributes
													a   =      self.change_stage(self.tableau							)
										else:
													a   =      self.pivot(self.tableau ,				__lowerCamelCase ,				__lowerCamelCase							)
							return {}





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					np.ndarray ,				__lowerCamelCase    :					list[str]							) ->					dict[str, float]:
							a   =      {"P": abs(tableau[0, -1]							)}

							for i in range(self.n_vars							):
										# Gives ids of nonzero entries in the ith column
										a   =      np.nonzero(tableau[:, i]							)
										a   =      len(nonzero[0]							)

										# First entry in the nonzero ids
										a   =      nonzero[0][0]
										a   =      tableau[nonzero_rowidx, i]

										# If there is only one nonzero value in column, which is one
										if n_nonzero == nonzero_val == 1:
													a   =      tableau[nonzero_rowidx, -1]
													a   =      rhs_val

        # Check for basic variables
							for title in col_titles:
										# Don't add RHS or slack variables to output dict
										if title[0] not in "R-s-a":
													output_dict.setdefault(__lowerCamelCase ,				0							)
							return output_dict


if __name__ == "__main__":
		import doctest

		doctest.testmod()






