




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
    "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
    "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
    "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
    "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
    "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
    "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
    "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
    "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"xlm"
     _a							 =						{
         "hidden_size": "emb_dim",
         "num_attention_heads": "n_heads",
         "num_hidden_layers": "n_layers",
         "n_words": "vocab_size",  # For backward compatibility
     }

     def __init__(  self		,     _a=3_0145		,     _a=2048		,     _a=12		,     _a=16		,     _a=0.1		,     _a=0.1		,     _a=True		,     _a=False		,     _a=False		,     _a=False		,     _a=1		,     _a=True		,     _a=512		,     _a=2048**-0.5		,     _a=1e-12		,     _a=0.02		,     _a=0		,     _a=1		,     _a=2		,     _a=3		,     _a=5		,     _a=True		,     _a="first"		,     _a=True		,     _a=None		,     _a=True		,     _a=0.1		,     _a=5		,     _a=5		,     _a=0		,     _a=0		,     _a=2		,     _a=0		,     **_a		,     )						->							Optional[int]:
           _A							:     Optional[int]			=						vocab_size
           _A							:     Optional[Any]			=						emb_dim
           _A							:     Optional[int]			=						n_layers
           _A							:     Optional[int]			=						n_heads
           _A							:     List[str]			=						dropout
           _A							:     Optional[int]			=						attention_dropout
           _A							:     Optional[Any]			=						gelu_activation
           _A							:     Union[str, Any]			=						sinusoidal_embeddings
           _A							:     Union[str, Any]			=						causal
           _A							:     List[str]			=						asm
           _A							:     int			=						n_langs
           _A							:     List[Any]			=						use_lang_emb
           _A							:     Any			=						layer_norm_eps
           _A							:     str			=						bos_index
           _A							:     Union[str, Any]			=						eos_index
           _A							:     Optional[Any]			=						pad_index
           _A							:     Optional[int]			=						unk_index
           _A							:     str			=						mask_index
           _A							:     Tuple			=						is_encoder
           _A							:     Dict			=						max_position_embeddings
           _A							:     Tuple			=						embed_init_std
           _A							:     Optional[Any]			=						init_std
           _A							:     Tuple			=						summary_type
           _A							:     Optional[int]			=						summary_use_proj
           _A							:     Optional[Any]			=						summary_activation
           _A							:     Dict			=						summary_proj_to_labels
           _A							:     Union[str, Any]			=						summary_first_dropout
           _A							:     Tuple			=						start_n_top
           _A							:     int			=						end_n_top
           _A							:     Optional[Any]			=						mask_token_id
           _A							:     Union[str, Any]			=						lang_id

           if "n_words" in kwargs:
                 _A							:     List[str]			=						kwargs["""n_words"""]

           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     **_a				)







class 						lowercase							(   UpperCamelCase__		):

     @property
     def 					a__	(  self				)						->							Mapping[str, Mapping[int, str]]:
           if self.task == "multiple-choice":
                 _A							:     Any			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                 _A							:     str			=						{0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
                   ("""token_type_ids""", dynamic_axis),
               ]				)





"""simple docstring"""




from __future__ import annotations

import unittest

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

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


if is_tf_available():
				import numpy
				import tensorflow as tf

				from transformers.models.esm.modeling_tf_esm import (
				    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEsmForMaskedLM,
				    TFEsmForSequenceClassification,
				    TFEsmForTokenClassification,
				    TFEsmModel,
				)
class 					__lowerCAmelCase					:


		'''simple docstring'''



		def __init__(  self							, _a							, ):
				__a           =			parent
				__a           =			13
				__a           =			7
				__a           =			True
				__a           =			True
				__a           =			True
				__a           =			99
				__a           =			32
				__a           =			2
				__a           =			4
				__a           =			37
				__a           =			'''gelu'''
				__a           =			0.1
				__a           =			0.1
				__a           =			512
				__a           =			16
				__a           =			2
				__a           =			0.02
				__a           =			3
				__a           =			4
				__a           =			None

		def 			__UpperCAmelCase       (  self      ):
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size      )

				__a           =			None
				if self.use_input_mask:
						__a           =			random_attention_mask([self.batch_size, self.seq_length]      )

				__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]							, self.num_choices      )

				__a           =			EsmConfig(
				    vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, pad_token_id=1							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, initializer_range=self.initializer_range							, )

				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

		def 			__UpperCAmelCase       (  self      ):
				(
				    (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     
				)           =			self.prepare_config_and_inputs()

				__a           =			True
				__a           =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
				__a           =			ids_tensor([self.batch_size, self.seq_length]							, vocab_size=2      )

				return (
				    config,
				    input_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			TFEsmModel(config=_a      )
				__a           =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				__a           =			model(_a      )

				__a           =			[input_ids, input_mask]
				__a           =			model(_a      )

				__a           =			model(_a      )

				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, ):
				__a           =			True

				__a           =			TFEsmModel(config=_a      )
				__a           =			{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''encoder_hidden_states''': encoder_hidden_states,
				    '''encoder_attention_mask''': encoder_attention_mask,
				}
				__a           =			model(_a      )

				__a           =			[input_ids, input_mask]
				__a           =			model(_a							, encoder_hidden_states=_a      )

				# Also check the case where encoder outputs are not passed
				__a           =			model(_a							, attention_mask=_a      )

				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			TFEsmForMaskedLM(config=_a      )
				__a           =			model([input_ids, input_mask]      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)      )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			self.num_labels
				__a           =			TFEsmForTokenClassification(config=_a      )
				__a           =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				__a           =			model(_a      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.num_labels)      )





		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.prepare_config_and_inputs()
				(
				    (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     (
				    __a
				)      ,     
				)           =			config_and_inputs
				__a           =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				return config, inputs_dict







@require_tf
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			int										=				(
		    (
		        TFEsmModel,
		        TFEsmForMaskedLM,
		        TFEsmForSequenceClassification,
		        TFEsmForTokenClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		__UpperCAmelCase		:			Tuple										=				(
		    {
		        'feature-extraction': TFEsmModel,
		        'fill-mask': TFEsmForMaskedLM,
		        'text-classification': TFEsmForSequenceClassification,
		        'token-classification': TFEsmForTokenClassification,
		        'zero-shot': TFEsmForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		__UpperCAmelCase		:			Tuple										=				False
		__UpperCAmelCase		:			Union[str, Any]										=				False

		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmModelTester(self      )
				__a           =			ConfigTester(self							, config_class=_a							, hidden_size=37      )

		def 			__UpperCAmelCase       (  self      ):
				self.config_tester.run_common_tests()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_a      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_a      )

		@slow
		def 			__UpperCAmelCase       (  self      ):
				for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__a           =			TFEsmModel.from_pretrained(_a      )
						self.assertIsNotNone(_a      )

		@unittest.skip('''Protein models do not support embedding resizing.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass

		@unittest.skip('''Protein models do not support embedding resizing.'''      )
		def 			__UpperCAmelCase       (  self      ):
				pass





		def 			__UpperCAmelCase       (  self      ):
				__a      ,     __a           =			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						__a           =			model_class(_a      )
						assert isinstance(model.get_input_embeddings()							, tf.keras.layers.Layer      )
						if model_class is TFEsmForMaskedLM:
								# Output embedding test differs from the main test because they're a matrix, not a layer
								__a           =			model.get_bias()
								assert isinstance(_a							, _a      )
								for k, v in name.items():
										assert isinstance(_a							, tf.Variable      )
						else:
								__a           =			model.get_output_embeddings()
								assert x is None
								__a           =			model.get_bias()
								assert name is None







@require_tf
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''      )

				__a           =			tf.constant([[0, 1, 2, 3, 4, 5]]      )
				__a           =			model(_a      )[0]
				__a           =			[1, 6, 33]
				self.assertEqual(list(output.numpy().shape      )							, _a      )
				# compare the actual values for a slice.
				__a           =			tf.constant(
				    [
				        [
				            [8.92_1518, -10.58_9814, -6.467_1307],
				            [-6.396_7156, -13.91_1377, -1.121_1915],
				            [-7.78_1247, -13.95_1557, -3.74_0592],
				        ]
				    ]      )
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							, expected_slice.numpy()							, atol=1E-2      )      )





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''      )

				__a           =			tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
				__a           =			model(_a      )[0]
				# compare the actual values for a slice.
				__a           =			tf.constant(
				    [
				        [
				            [0.1444_3092, 0.5412_5327, 0.324_7739],
				            [0.3034_0484, 0.0052_6676, 0.3107_7722],
				            [0.3227_8043, -0.2498_7096, 0.341_4628],
				        ]
				    ]      )
				self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()							, expected_slice.numpy()							, atol=1E-4      )      )






